R package WGCNA: Weighted Correlation Network Analysis. Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data. Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to sample traits. Also includes a number of utility functions for data manipulation and visualization.

References in zbMATH (referenced in 22 articles )

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  1. Austen Bernardi, Jessica M.J. Swanson: CycFlowDec: A Python module for decomposing flow networks using simple cycles (2021) not zbMATH
  2. Ma, Chen; Yao, Zhihao; Zhang, Qinran; Zou, Xiufen: Quantitative integration of radiomic and genomic data improves survival prediction of low-grade glioma patients (2021)
  3. Wang, Y. X. Rachel; Li, Lexin; Li, Jingyi Jessica; Huang, Haiyan: Network modeling in biology: statistical methods for gene and brain networks (2021)
  4. Yu, Chong; Xu, Hong; Wang, Jin: A global and physical mechanism of gastric cancer formation and progression (2021)
  5. Daniel Conn, Tuck Ngun, Gang Li, Christina M. Ramirez: Fuzzy Forests: Extending Random Forest Feature Selection for Correlated, High-Dimensional Data (2019) not zbMATH
  6. Kharoubi, Rachid; Oualkacha, Karim; Mkhadri, Abdallah: The cluster correlation-network support vector machine for high-dimensional binary classification (2019)
  7. Melina Vidoni; Aldo Vecchietti : rsppfp: An R package for the shortest path problem with forbidden paths (2019) not zbMATH
  8. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  9. Suner, Aslı: Clustering methods for single-cell RNA-sequencing expression data: performance evaluation with varying sample sizes and cell compositions (2019)
  10. Yuan, Ye; Bar-Joseph, Ziv: Deep learning for inferring gene relationships from single-cell expression data (2019)
  11. Bodwin, Kelly; Zhang, Kai; Nobel, Andrew: A testing based approach to the discovery of differentially correlated variable sets (2018)
  12. Esteves, Gustavo H.; Reis, Luiz F. L.: A statistical method for measuring activation of gene regulatory networks (2018)
  13. Md. Bahadur Badsha, Evan A Martin, Audrey Qiuyan Fu: MRPC: An R package for accurate inference of causal graphs (2018) arXiv
  14. von Stechow, Louise (ed.); Delgado, Alberto Santos (ed.): Computational cell biology. Methods and protocols (2018)
  15. Deisy Morselli Gysi, Andre Voigt, Tiago de Miranda Fragoso, Eivind Almaas, Katja Nowick: wTO: an R package for computing weighted topological overlap and consensus networks with an integrated visualization tool (2017) arXiv
  16. Weishaupt, Holger; Johansson, Patrik; Engström, Christopher; Nelander, Sven; Silvestrov, Sergei; Swartling, Fredrik J.: Loss of conservation of graph centralities in reverse-engineered transcriptional regulatory networks (2017)
  17. Blum, Yuna; Houée-Bigot, Magalie; Causeur, David: Sparse factor model for co-expression networks with an application using prior biological knowledge (2016)
  18. Liu, Li; Lei, Jing; Roeder, Kathryn: Network assisted analysis to reveal the genetic basis of autism (2015)
  19. Qin, Huaizhen; Ouyang, Weiwei: Statistical properties of gene-gene correlations in omics experiments (2015)
  20. Lu, Xinguo; Deng, Yong; Huang, Lei; Feng, Bingtao; Liao, Bo: A co-expression modules based gene selection for cancer recognition (2014)

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